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RMT-Net: Reject-Aware Multi-Task Network for Modeling missing-not-at-random Data in Financial Credit Scoring

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第7期35卷 7427-7439页

作者： Liu, Qiang Luo, Yingtao Wu, Shu Zhang, Zhen Yue, Xiangnan Jin, Hong Wang, Liang Chinese Acad Sci CASIA Inst Automat Ctr Res Intelligent Percept & Comp CRIPAC Natl Lab Pattern Recognit NLPR Beijing Peoples R China Carnegie Mellon Univ H John Heinz III Sch Informat Syst & Management Pittsburgh PA 15213 USA Ant Grp Hangzhou 310000 Peoples R China

In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejected samples, which leads to missing-not-at-random selection bias. Machine learning models trained on such biased data are inevitably unreliable. In this work, we find that the default/non-default classification task and the rejection/approval classification task are highly correlated, according to both real-world data study and theoretical analysis. Consequently, the learning of default/non-default can benefit from rejection/approval. Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL). Specifically, we propose a novel Reject-aware Multi-Task Network (RMT-Net), which learns the task weights that control the information sharing from the rejection/approval task to the default/non-default task by a gating network based on rejection probabilities. RMT-Net leverages the relation between the two tasks that the larger the rejection probability, the more the default/non-default task needs to learn from the rejection/approval task. Furthermore, we extend RMT-Net to RMT-Net++ for modeling scenarios with multiple rejection/approval strategies. Extensive experiments are conducted on several datasets, and strongly verifies the effectiveness of RMT-Net on both approved and rejected samples. In addition, RMT-Net++ further improves RMT-Net's performances.

关键词： Task analysis Data models Multitasking Biological system modeling Support vector machines Training Computational modeling Credit scoring default prediction missing-not-at-random Index Terms multi-task learning reject inference

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A Robust Classifier under missing-not-at-random Sample Selection Bias

A Robust Classifier under Missing-Not-at-Random Sample Selec...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Mai, Huy Huang, Wen Du, Wei Wu, Xintao University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350324457

The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the training set. Although there are many approaches proposed to learn a classifier under sample selection bias, few address the case where a subset of labels in the training set are missing-not-at-random (MNAR) as a result of the selection process. In statistics, Greene's method formulates this type of sample selection with logistic regression as the prediction model. However, we find that simply integrating this method into a robust classification framework is not effective for this bias setting. In this paper, we propose BiasCorr, an algorithm that improves on Greene's method by modifying the original training set in order for a classifier to learn under MNAR sample selection bias. We provide theoretical guarantee for the improvement of BiasCorr over Greene's method by analyzing its bias. Experimental results on real-world datasets demonstrate that BiasCorr produces robust classifiers and can be extended to outperform state-of-the-art classifiers that have been proposed to train under sample selection bias. © 2023 IEEE.

关键词： missing-not-at-random Robust classifier sample selection bias

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Trial arm outcome variance difference after dropout as an indicator of missing-not-at-random bias in randomized controlled trials

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BIOMETRICAL JOURNAL 2023年第8期65卷 e2200116页

作者： Hazewinkel, Audinga-Dea Tilling, Kate Wade, Kaitlin H. Palmer, Tom Univ Bristol Bristol Med Sch Populat Hlth Sci Bristol England Univ Bristol Bristol Med Sch Med Res Council Integrat Epidemiol Unit Bristol England Univ Bristol Bristol Med Sch Populat Hlth Sci Audinga Dea Hazewinkel Oakfield House Oakfield G Bristol BS8 2BN England

randomized controlled trials (RCTs) are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete case analysis (CCA) and multiple imputation (MI) may be biased. There is no statistical test for distinguishing between outcomes missing at random (MAR) and MNAR. Current strategies rely on comparing dropout proportions and covariate distributions, and using auxiliary information to assess the likelihood of dropout being associated with the outcome. We propose using the observed variance difference across trial arms as a tool for assessing the risk of dropout being MNAR in RCTs with continuous outcomes. In an RCT, at randomization, the distributions of all covariates should be equal in the populations randomized to the intervention and control arms. Under the assumption of homogeneous treatment effects and homoskedastic outcome errors, the variance of the outcome will also be equal in the two populations over the course of follow-up. We show that under MAR dropout, the observed outcome variances, conditional on the variables included in the model, are equal across trial arms, whereas MNAR dropout may result in unequal variances. Consequently, unequal observed conditional trial arm variances are an indicator of MNAR dropout and possible bias of the estimated treatment effect. Heterogeneous treatment effects or heteroskedastic outcome errors are another potential cause of observing different outcome variances. We show that for longitudinal data, we can isolate the effect of MNAR outcome-dependent dropout by considering the variance difference at baseline in the same set of patients who are observed at final follow-up. We illustrate our method in simulation for CCA and MI, and in applications using individual-level data and summary data.

关键词： bias dropout missing-not-at-random randomized controlled trials risk-of-bias

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Learning Without missing-At-random Prior Propensity-A Generative Approach for Recommender Systems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 754-765页

作者： Xu, Yuanbo Zhuang, Fuzhen Wang, En Li, Chaozhuo Wu, Jie Jilin Univ Dept Comp Sci & Technol Changchun 130012 Peoples R China Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci SKLSDE Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv MoE Beijing 100876 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

In recommender systems, it is frequently presumed that missing ratings adhere to a missing at random (MAR) mechanism, implying the absence of ratings is independent of their potential values. However, this assumption fails to hold in real-world scenarios, where users are inclined to rate items they either strongly favor or disfavor, introducing a missing not at random (MNAR) scenario. To tackle this issue, prior researchers have utilized explicit MAR feedbacks to infer the propensities of unobserved, implicit MNAR feedbacks. Nonetheless, acquiring explicit MAR feedbacks is resource-intensive and time-consuming and may not reflect users' true preferences. Furthermore, most methods have only been tested on synthetic or small-scale datasets, thus their applicability and effectiveness in real-world settings without MAR feedbacks remain unclear. Along these lines, we aim to predict MNAR ratings without MAR prior propensities by exploring the consistency between MAR and MNAR feedbacks and narrowing the gap between them. From the empirical study and preliminary experiment, we hypothesize that user preferences can be treated as the common prior propensity for both MAR and MNAR generative processes. In this way, we extend this hypothesis to a more general MNAR scenario: user preferences learned from MNAR can partially substitute for the prior propensities derived from MAR feedbacks for MNAR recommendation tasks. To validate our hypothesis and approach, we develop a lightweight iterative probabilistic matrix factorization framework (lightIPMF) as a practical method of our methodology, utilizing user preferences extracted from MNAR, not MAR, to estimate MNAR feedbacks. Finally, the experimental results show that modeling user preferences can effectively improve MNAR feedback estimation without MAR feedback, and our proposed lightIPMF outperforms the state-of-the-art MNAR methods in predicting MNAR feedbacks.

关键词： Mars Recommender systems Probabilistic logic Predictive models Estimation Sparse matrices Iterative methods Data models Testing Technological innovation Generative model missing-not-at-random recommender systems

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Asymmetric Tri-training for Debiasing missing-not-at-random Explicit Feedback 20

Asymmetric Tri-training for Debiasing Missing-Not-At-Random ...

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43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Saito, Yuta Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781450380164

In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the learning of a recommender with biased feedback is one of the most challenging problems, as it is widely known that naive approaches under selection bias often lead to suboptimal results. A well-established solution for the problem is using propensity scoring techniques. The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of its propensity. However, the performance of the propensity-based unbiased estimation approach is often affected by choice of the propensity estimation model or the high variance problem. To overcome these limitations, we propose a model-agnostic meta-learning method inspired by the asymmetric tri-training framework for unsupervised domain adaptation. The proposed method utilizes two predictors to generate data with reliable pseudo-ratings and another predictor to make the final predictions. In a theoretical analysis, a propensity-independent upper bound of the true performance metric is derived, and it is demonstrated that the proposed method can minimize this bound. We conduct comprehensive experiments using public real-world datasets. The results suggest that the previous propensity-based methods are largely affected by the choice of propensity models and the variance problem caused by the inverse propensity weighting. Moreover, we show that the proposed meta-learning method is robust to these issues and can facilitate in developing effective recommendations from biased explicit feedback.

关键词： recommender systems missing-not-at-random selection bias explicit feedback matrix factorization unsupervised domain adaptation

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Unbiased Recommender Learning from missing-not-at-random Implicit Feedback 20

Unbiased Recommender Learning from Missing-Not-At-Random Imp...

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13th Annual ACM International Conference on Web Search and Data Mining (WSDM)

作者： Saito, Yuta Yaginuma, Suguru Nishino, Yuta Sakata, Hayato Nakata, Kazuhide Tokyo Inst Technol Tokyo Japan SMN Corp Fukuoka Japan

ISBN: (纸本)9781450368223

Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for rare items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.

关键词： Implicit Feedback missing-not-at-random Inverse Propensity Weighting Positive-Unlabeled Learning Matrix Factorization

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Analysis of multiple-variable missing-not-at-random survey data for child lead surveillance using NHANES

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STATISTICS IN MEDICINE 2016年第29期35卷 5417-5429页

作者： Roberts, Eric M. English, Paul B. Inst Publ Hlth Oakland CA 94607 USA Calif Dept Publ Hlth 850 Marina Bay PkwyBldg P-3 Richmond CA 94804 USA

Background - Although ongoing, multi-topic surveys form the basis of public health surveillance in many countries, their utility for specific subject matter areas can be limited by high proportions of missing data. For example, the National Health and Examination Survey is the main resource for surveillance of elevated blood lead levels (EBLLs) in US children, but key predictor variables are missing for as many as 35% of respondents. Methods - Using a Bayesian framework, we formulate a t-distributed Heckman selection model applicable to the case of multiple missing-not-at-random variables in the context of a complex survey design. We demonstrate the utility of the results by calculating prevalence estimates for lead levels exceeding 2.5, 5.0, and 10.0 mu g/dL among children 1 to 5 years of age for a variety of time points and geographies by applying the coefficients to data from the American Community Survey from the US Census. Results - We present a protocol for estimating posterior distributions of parameters using Gibbs and grid sampling steps. Stark disparities in the prevalence of EBLL by race/ethnicity, age of housing, and poverty are readily quantified, and three-to five-fold differences in predicted prevalence across geographies within the US are presented. Conclusions - We are able to conduct multivariate analyses of EBLLs that incorporate the crucial variable age of housing, analyses that have not been previously available using these data. This represents an expansion of the utility of National Health and Examination Survey that is likely to be relevant to many similar ongoing, multi-topic health surveillance efforts. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： selection models survey data missing-not-at-random lead poisoning

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On Prediction Feature Assignment in the Heckman Selection Model

On Prediction Feature Assignment in the Heckman Selection Mo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mai, Huy Wu, Xintao Univ Arkansas Dept Elect Engn & Comp Sci Fayetteville AR 72701 USA

ISBN: (纸本)9798350359329;9798350359312

Under missing-not-at-random (MNAR) sample selection bias, the performance of a prediction model is often degraded. This paper focuses on one classic instance of MNAR sample selection bias where a subset of samples have non-randomly missing outcomes. The Heckman selection model and its variants have commonly been used to handle this type of sample selection bias. The Heckman model uses two separate equations to model the prediction and selection of samples, where the selection features include all prediction features. When using the Heckman model, the prediction features must be properly chosen from the set of selection features. However, choosing the proper prediction features is a challenging task for the Heckman model. This is especially the case when the number of selection features is large. Existing approaches that use the Heckman model often provide a manually chosen set of prediction features. In this paper, we propose Heckman-FA as a novel data-driven framework for obtaining prediction features for the Heckman model. Heckman-FA first trains an assignment function that determines whether or not a selection feature is assigned as a prediction feature. Using the parameters of the trained function, the framework extracts a suitable set of prediction features based on the goodness-of-fit of the prediction model given the chosen prediction features and the correlation between noise terms of the prediction and selection equations. Experimental results on real-world datasets show that Heckman-FA produces a robust regression model under MNAR sample selection bias.

关键词： sample selection bias missing-not-at-random Heckman selection model robust regression

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Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control 23

Unbiased Pairwise Learning from Implicit Feedback for Recomm...

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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Ren, Yi Tang, Hongyan Rong, Jiangpeng Zhu, Siwen Tencent Beijing Peoples R China

ISBN: (纸本)9781450394086

Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback. First, implicit data just includes positive feedback. Therefore, we are not sure whether the non-interacted items are really negative or positive but not displayed to the corresponding user. Moreover, the relevance of rare items is usually underestimated since much fewer positive feedback of rare items is collected compared with popular ones. To tackle such difficulties, both pointwise and pairwise solutions are proposed before for unbiased relevance learning. As pairwise learning suits well for the ranking tasks, the previously proposed unbiased pairwise learning algorithm already achieves state-of-the-art performance. Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias. In this work, we propose an unbiased pairwise learning method, named UPL, with much lower variance to learn a truly unbiased recommender model. Extensive offline experiments on real world datasets and online A/B testing demonstrate the superior performance of our proposed method.

关键词： Recommender Systems Unbiased Learning Implicit Feedback missing-not-at-random Inverse Propensity Score

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Sequential Nature of Recommender Systems Disrupts the Evaluation Process 1

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3rd International Workshop on Algorithmic Bias in Search and Recommendation (BIAS) held as part of the 43rd European Conference on Information Retrieval (ECIR)

作者： Shirali, Ali Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9783031093166

ISBN: (纸本)9783031093166;9783031093159

Datasets are often generated in a sequential manner, where the previous samples and intermediate decisions or interventions affect subsequent samples. This is especially prominent in cases where there are significant human-AI interactions, such as in recommender systems. To characterize the importance of this relationship across samples, we propose to use adversarial attacks on popular evaluation processes. We present sequence-aware boosting attacks and provide a lower bound on the amount of extra information that can be exploited from a confidential test set solely based on the order of the observed data. We use real and synthetic data to test our methods and show that the evaluation process on the MovieLense-100k dataset can be affected by similar to 1% which is important when considering the close competition. Codes are publicly available (https://***/alishiraliGit/augmented-boosting-attack).

关键词： Sequential Decision Making Sequential Recommender System Evaluation Mechanisms missing-not-at-random

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